NAHGNN：邻域感知异构图神经网络

李强; 郑唯; 陈明; 谭兴义; 马华

doi:10.12263/DZXB.20250420

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NAHGNN：邻域感知异构图神经网络

学术论文 | 更新时间：2026-02-10

- NAHGNN：邻域感知异构图神经网络
- NAHGNN: Neighborhood Aware Heterogeneous Graph Neural Network
- 电子学报 2025年53卷第11期页码：4142-4156
- 作者机构：
  
  湖南师范大学信息科学与工程学院，湖南长沙 410081
- 作者简介：
  
  [ "李强男，1979年1月出生于江西省新余市.现为湖南师范大学信息科学与工程学院副教授.主要研究方向为边缘智能、智能计算.E-mail: liqiang@hunnu.edu.cn" ]
  [ "郑唯男，2000年6月出生于江苏省扬州市.现为湖南师范大学软件工程专业硕士研究生.主要研究方向为异构图表示学习.E-mail: zw2945175993@163.com" ]
  [ "陈明女，1983年6月出生于湖南省祁阳市.现为湖南师范大学信息科学与工程学院副教授.主要研究方向为图机器学习及其应用.E-mail: chenming@hunnu.edu.cn" ]
  [ "谭兴义女，2000年10月出生于广西壮族自治区防城港市.现为湖南师范大学软件工程专业硕士研究生.主要研究方向为边缘智能.E-mail: tanxingyi899@163.com" ]
  [ "马华男，1979年9月出生于湖南省宁远县.现为湖南师范大学信息科学与工程学院教授和博士生导师.主要研究方向为智能计算与服务计算、大数据挖掘.E-mail: huama@hunnu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62477009);湖南省自然科学基金(2023JJ30411);湖南省重大科技创新平台项目(2024JC1003);湖南省科技计划(2021GK5014)
- DOI：10.12263/DZXB.20250420
  中图分类号： TP391
- 收稿：2025-05-26，
  
  录用：2025-11-03，
  
  纸质出版：2025-11-25
- 稿件说明：
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李强, 郑唯, 陈明, 等. NAHGNN：邻域感知异构图神经网络[J]. 电子学报, 2025, 53(11): 4142-4156.

LI Qiang, ZHENG Wei, CHEN Ming, et al. NAHGNN: Neighborhood Aware Heterogeneous Graph Neural Network[J]. Acta Electronica Sinica, 2025, 53(11): 4142-4156.
李强, 郑唯, 陈明, 等. NAHGNN：邻域感知异构图神经网络[J]. 电子学报, 2025, 53(11): 4142-4156. DOI：10.12263/DZXB.20250420

LI Qiang, ZHENG Wei, CHEN Ming, et al. NAHGNN: Neighborhood Aware Heterogeneous Graph Neural Network[J]. Acta Electronica Sinica, 2025, 53(11): 4142-4156. DOI：10.12263/DZXB.20250420

摘要

异构图广泛存在于社交网络、推荐系统和生物网络等复杂场景中.基于元路径的异构图神经网络通过定义高阶语义路径对跨类型间接关系进行显式建模，以提升复杂关系建模能力.但现有研究或未加区分地使用指定长度内所有的元路径特征，随着元路径长度的增加，所生成特征的数量呈指数上升，造成语义信息的冗余；或受限于高阶聚合导致的过平滑现象，造成边缘信息的丢失.为解决这些问题，本文提出了一种异构图神经网络模型（Neighborhood Aware Heterogeneous Graph Neural Network，NAHGNN），从邻域感知的角度切入，通过任务解耦，将特征生成分为两个步骤：关联元路径生成和邻域感知特征聚合.首先，关联元路径生成模块利用起始节点与结束节点均是目标节点类型的关联元路径特征，学习目标节点间丰富的语义信息.其次，从目标节点的邻域感知方式出发，设计了一个简单高效的邻域感知特征聚合模块，对关联元路径中忽略的邻域信息进行提取.最后，为了拟合相应邻域感知方式的语义表示，避免邻域感知特征间相互影响，设计了一个带掩码的语义融合模块，融合不同特征间的语义信息.在DBLP、ACM、IMDB和Freebase四个公开异构图数据集上与六种主流异构图神经网络基线进行实验对比.结果表明，NAHGNN在节点分类任务中Micro-F1提升幅度为0.63~12.50个百分点，训练时间与GPU内存消耗显著下降，并展现出良好的可解释性.

Abstract

Heterogeneous graphs are widely present in complex scenarios such as social networks

recommendation systems

and biological networks. Meta-path-based heterogeneous graph neural networks (HGNNs) explicitly model cross-type indirect relationships via high-order semantic paths

enhancing the ability to capture complex dependencies. However

existing studies either use all meta-path features within a specified length without distinction

leading to redundancy in semantic information as the number of generated features rises exponentially with the increase of the meta-paths length

or suffer from over-smoothing caused by high-order aggregation

resulting in the loss of edge information. To address these issues

this paper proposes a neighborhood aware heterogeneous graph neural network (NAHGNN). From the perspective of neighborhood awareness and through task decoupling

the feature generation is divided into two steps: associative meta-path generation and neighborhood-aware feature aggregation. Firstly

an associative meta-path generation module learns rich semantic information between target nodes by leveraging associative meta-path features that both start and end nodes are of the target type. Secondly

a simple and efficient neighborhood-aware feature aggregation module is designed based on the neighborhood-aware modalities of target nodes to extract neglected neighborhood information in associative meta-paths. Finally

to fit the semantic representations of corresponding neighborhood-aware modalities and avoid mutual interference between neighborhood-aware features

a semantic fusion module with a band mask is designed to integrate semantic information across different features. Experimental comparisons are conducted with six mainstream heterogeneous graph neural network baselines on four public heterogeneous graph datasets (DBLP

ACM

IMDB

and Freebase). The results show that NAHGNN achieves a Micro-F1 improvement of 0.63 to 12.50 percentage points in node classification tasks

significantly reduces training time and GPU memory consumption

and exhibits favorable interpretability.

关键词

Keywords

references

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